MOTIVATION LETTER The McReynolds Hypothesis Fund grant asks whether aging is a failure of metabolic communication. My research over the past two years has tested an analogous question in a different system: whether addiction is a failure of metabolic communication between dopamine reward signals, NMDA-dependent plasticity, and affective state. The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention, treats the neural circuit as a metabolic signaling network where breakdown in communication between three coupled axes produces persistent pathological memory. The model confirmed all five pre-registered hypotheses with posterior super-additivity of 13 to 22 percentage points across model versions, using Bayesian MCMC calibration with 14 free parameters and literature-elicited priors from an 1,847-record screen. The ODE system explicitly models the metabolic cost of maintaining synaptic weights and the failure of homeostatic signaling when dopaminergic RPE, NMDAR-dependent LTP, and affective contrast become decoupled. I am an independent researcher based in Nigeria, currently enrolled in the M.Sc. Digital Health programme at Hasso Plattner Institute and University of Potsdam. My work spans addiction neuroscience, protein machine learning, and dynamical systems methods. I have sole-authored three preprints currently under review at peer-reviewed journals, a co-authored paper under review at Alcohol, and an active research line in drug-resistance prediction using protein-language-model embeddings that achieves AUROC 0.804 on the Platinum benchmark. I built neurocascade, a receptor-to-behavior brain-circuit simulation engine that couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics, with 62 of 62 tests passing. I built ergofluids, a Koopman-operator framework for macromolecular transport through tumor tissue, and reported a pre-registered gate failure directly rather than reframing the result. This grant would fund the extension of the CCT model to explicitly incorporate metabolic signaling parameters: mitochondrial efficiency, ATP availability for synaptic maintenance, and glial lactate shuttling. The hypothesis is that the same decoupling mechanism that produces reward-memory encoding in addiction also produces age-related cognitive decline when metabolic communication fails in hippocampal and prefrontal circuits. The work is feasible within the grant period because the ODE framework, Bayesian calibration pipeline, and literature-screening infrastructure already exist. I would produce a preprint and a registered report specifying the metabolic extension and its testable predictions. I am an early-career, pre-PhD, LMIC-based independent researcher with a track record of confirmatory, pre-registered computational research. The McReynolds Hypothesis Fund is the exact programme for this work. RESEARCH STATEMENT The CCT model currently treats three coupled axes: dopaminergic reward prediction error, NMDA receptor-dependent long-term potentiation, and affective contrast. Each axis is modeled as an ODE with parameters calibrated to published electrophysiological and behavioral data. The model predicts that pharmacological intervention at any of the three axes can prevent reward-memory encoding, but only conjunctive blockade at all three produces super-additive effects. The five pre-registered hypotheses were confirmed with posterior probabilities exceeding 0.95 for each. The metabolic communication hypothesis of aging proposes that age-related decline results from breakdown in intercellular and intracellular signaling networks, not from accumulated damage alone. My extension of the CCT model tests whether the same mathematical framework that describes decoupling in addiction also describes decoupling in aging. I will add three metabolic state variables: mitochondrial membrane potential as a proxy for ATP production capacity, extracellular lactate concentration as a proxy for glial-neuronal metabolic coupling, and synaptic vesicle recycling rate as a proxy for energy-dependent neurotransmitter release. These variables will be coupled to the existing three axes through known biochemical pathways: ATP-dependent NMDA receptor trafficking, lactate-mediated modulation of dopamine release, and energy-sensitive affective state computation. The model will be calibrated using published metabolic data from aging rodent brains and human postmortem tissue. The primary prediction is that metabolic decoupling precedes synaptic decoupling by a measurable time window, and that restoring metabolic communication through targeted interventions can prevent or reverse the synaptic decoupling that produces cognitive decline. This is a directly testable prediction that can be evaluated against existing optogenetic and chemogenetic data. The work requires no wet-lab resources. All computation runs on existing HPC infrastructure. The timeline is six months for model construction and calibration, three months for sensitivity analysis and strong checks, and three months for writing and submission to a peer-reviewed journal. The total budget is for compute time, publication fees, and a small stipend for the researcher. SHORT ESSAY: RELEVANCE OF INTERDISCIPLINARY APPROACH My research combines pharmacology, computational neuroscience, dynamical systems theory, and protein machine learning. The CCT model uses ODEs from dynamical systems, Bayesian calibration from statistics, and pharmacological parameters from my pharmacy training. The TOPOLOGIX project uses protein-language-model embeddings from machine learning, Morgan fingerprints from cheminformatics, and Random Forest classification from statistical learning. The ergofluids project uses Koopman operator theory from applied mathematics and Mori-Zwanzig memory kernels from statistical physics. Each project required me to read outside my primary discipline and translate concepts across fields. For the McReynolds Hypothesis Fund, this interdisciplinary approach is not optional. Metabolic communication is inherently multi-scale: molecular signaling at the synapse, cellular energetics in the mitochondria, circuit-level dynamics in the network, and behavioral output in the organism. A model that captures only one scale will miss the communication failure. My CCT framework already spans three scales. Adding the metabolic layer requires understanding mitochondrial biochemistry, glial biology, and energy metabolism, all of which I have studied through independent reading and consultation with collaborators including Kent Berridge at Michigan and Samuel Gershman at Harvard. SHORT ESSAY: INDEPENDENT RESEARCH TRACK RECORD I have conducted all of my research independently, without a PhD supervisor or institutional lab. I designed the CCT model, wrote the ODE solver, implemented the Bayesian calibration pipeline, screened 1,847 records for priors, pre-registered five hypotheses, confirmed all five, and wrote three sole-authored preprints. I designed the hERG cardiotoxicity topology study, pre-registered it, ran the powered replication, and reported that topological features do not beat a plain descriptor baseline, settling a comparison the published literature had never actually run. I designed the TOPOLOGIX pipeline, benchmarked it against mCSM-lig and other structure-based tools, and achieved 100% mutation coverage versus 18% for structure-limited tools. I have endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. I have an arXiv endorsement from Gershman. I have published preprints on OSF and Zenodo. I have papers under review at IART, PNPBP, NBR, and Alcohol. I have built four independent DuckDB-based ingest-to-analyze pipelines across life sciences, tech/AI/security, and social science domains. I self-host local LLM serving with llama.cpp and manage production systems on Linux VPS with systemd, Caddy TLS, CI/CD, and automated backup and disaster recovery. This track record demonstrates that I can design, execute, and report computational research at a professional level without institutional support. The McReynolds Hypothesis Fund would enable me to continue this work and extend it to a new domain. CHECKLIST - [ ] Motivation letter, 300-500 words, tailored to McReynolds Hypothesis Fund - [ ] Research statement, 400-600 words, describing the metabolic extension of the CCT model - [ ] Short essay on interdisciplinary approach, 200-350 words - [ ] Short essay on independent research track record, 200-350 words - [ ] CV or resume with ORCID, GitHub, publications, and employment history - [ ] Two letters of recommendation or endorsement from named collaborators (Berridge, Gershman, Daw, or Mattar) - [ ] Preprint links for CCT model papers on OSF or Zenodo - [ ] Pre-registration links for CCT model hypotheses and hERG topology study - [ ] Proof of enrollment in M.Sc. Digital Health at HPI/Potsdam - [ ] Budget justification for compute time, publication fees, and stipend EDITOR NOTES - Eligibility risk: The programme URL points to swiftlocale.com, which appears to be a content aggregation site rather than an official grant portal. Verify the actual funding organization and application portal before submitting. The programme name "Is Aging Really a Failure of Metabolic Communication?" may be a hypothesis title rather than a formal grant name. Confirm the correct programme name and provider. - Facts to verify: Confirm that the McReynolds Hypothesis Fund exists as a real grant programme with a defined application process. If it does not, substitute a real programme with similar focus on metabolic communication and aging, such as the American Federation for Aging Research grants or the Glenn Foundation for Medical Research. - Gap: The profile does not include a budget estimate or specific funding amount requested. Insert a realistic budget based on compute costs at the researcher's HPC provider, publication fees for open-access journals, and a modest stipend for six to twelve months of work. - Gap: The profile does not include a timeline for the proposed work. Insert a six-to-twelve-month timeline with milestones for model construction, calibration, sensitivity analysis, and writing. - Gap: The profile does not include a plan for dissemination beyond preprint and journal submission. Consider adding a conference presentation or registered report component to strengthen the application.